Inequity In Corrective Eyewear Insurance In Ontario: A Repeated Cross-Sectional Study
Bibliographic record
Abstract
Background Although the lack of vision insurance coverage has been linked to adverse vision outcomes, Canada still has a patchwork system that provides poor or no coverage to many of its residents. Data and methods We used data from the Canadian Community Health Survey (2005, 2008, 2013-2014) and logistic regressions to describe the extent to which Ontario residents reported insurance coverage for corrective eyewear after the delisting of routine eye examinations for healthy adults in 2004; and, to examine associations between socioeconomic and demographic characteristics, self-reported health and insurance coverage for corrective eyewear. Results We found important socioeconomic differences in the reporting of corrective eyewear insurance. Lower-SES adults were more likely to have reported public corrective eyewear coverage, whereas higher-SES adults and older adults were more likely to have reported private coverage. Overall, lower-SES adults and older adults were substantially less likely to have reported any corrective eyewear coverage. Adults and older adults in poorer health had lower odds of having reported private coverage for corrective eyewear. Relative to 2005, adults had higher odds of having reported public coverage, while older adults had lower odds of having reported public coverage for corrective eyewear in 2013 and 2014. Interpretation Our findings reinforce the limits of the current patchwork insurance system for eye care and eyewear in Ontario. The substantial socioeconomic differences in the reporting of corrective eyewear insurance, as well as the low coverage in older adults, particularly among the poor and unhealthy, are of concern.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".